Порівняння методів

	Сиамська нейронна мережа	Трансферне навчання
Галузь	Глибоке навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	1993	2010 (formalized); 1990s (early roots)
Автор методу	Jane Bromley & Yann LeCun et al.; popularized by Koch et al.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Deep metric-learning architecture	Learning paradigm
Основоположне джерело	Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1993). Signature verification using a 'Siamese' time delay neural network. Advances in Neural Information Processing Systems, 6.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Інші назви	twin network, Siamese neural network, contrastive metric network, Siyam ağı	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Пов'язані	1	3
Підсумок	A Siamese network is a deep architecture with two (or more) identical, weight-sharing branches that map inputs into an embedding space where similar inputs land close together and dissimilar ones far apart. Introduced by Bromley, LeCun, and colleagues in 1993 for signature verification and revived by Koch et al. (2015) for one-shot image recognition, it learns a similarity metric rather than fixed class labels, making it ideal for verification, matching, and few-shot tasks.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.